20 research outputs found
Web Living Case: A Web Based Business Case Delivery System for Collaborative Work
This paper describes the Web Living Case (WLC), a Web based business case delivery system that incorporates support for collaborative work. WLC provides a more interesting environment for case presentation than do traditional written cases. To facilitate effective collaboration, WLC provides shared workspaces for students working on common tasks, bulletin boards, and real-time conversation support, and a consistent and friendly user-interface
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An Active Learning Approach for Rapid Characterization of Endothelial Cells in Human Tumors
Currently, no available pathological or molecular measures of tumor angiogenesis predict response to antiangiogenic therapies used in clinical practice. Recognizing that tumor endothelial cells (EC) and EC activation and survival signaling are the direct targets of these therapies, we sought to develop an automated platform for quantifying activity of critical signaling pathways and other biological events in EC of patient tumors by histopathology. Computer image analysis of EC in highly heterogeneous human tumors by a statistical classifier trained using examples selected by human experts performed poorly due to subjectivity and selection bias. We hypothesized that the analysis can be optimized by a more active process to aid experts in identifying informative training examples. To test this hypothesis, we incorporated a novel active learning (AL) algorithm into FARSIGHT image analysis software that aids the expert by seeking out informative examples for the operator to label. The resulting FARSIGHT-AL system identified EC with specificity and sensitivity consistently greater than 0.9 and outperformed traditional supervised classification algorithms. The system modeled individual operator preferences and generated reproducible results. Using the results of EC classification, we also quantified proliferation (Ki67) and activity in important signal transduction pathways (MAP kinase, STAT3) in immunostained human clear cell renal cell carcinoma and other tumors. FARSIGHT-AL enables characterization of EC in conventionally preserved human tumors in a more automated process suitable for testing and validating in clinical trials. The results of our study support a unique opportunity for quantifying angiogenesis in a manner that can now be tested for its ability to identify novel predictive and response biomarkers
Active and Transfer learning Methods for Computational Histology
Tissue micro-environments of critical interest like tumors, stem-cell niches, and brain tissue surrounding implanted neuroprosthetic devices are complex in structure and harbor complex processes. Understanding events and perturbations that occur in these micro-environments entails selective molecular imaging of the tissues, delineating cellular structures, and accurate cell classification. The algorithm presented in this thesis advances the state of the art in cell classification in large scale histological studies.
The core contribution of this thesis is a novel active machine learning algorithm that leverages the advances made in the fields of optimal experimental design and submodular functions. In large and diverse datasets, manually annotating examples to create a training set is effort intensive and suboptimal due to subjectivity and selection bias introduced by human experts. The proposed algorithm reduces human effort and eliminates subjectivity by actively participating in the learning process to select informative examples for the user to label. The algorithm selects multiple informative examples in a learning iteration reducing the burden of retraining the classifier multiple times. The algorithm relies on the submodularity property of the D-optimal criterion to provide performance guarantees for the examples selected for labeling. The algorithm also obviates the necessity for performing offline analysis for feature selection by using the popular LASSO technique to perform feature selection during training. Our experiments on multiple real world data from clinical studies show that the proposed active learning algorithm outperforms standard learning and other active learning frameworks.
Since histological studies involve analysis of similar cells under different conditions, the labeling effort to classify similar or related cells in different tissues or conditions can further be reduced by leveraging knowledge learned from one classification task and using it for a related task. The proposed algorithm is also extended to a transfer learning setting to take advantage of existing labeled data sets even when they are mismatched. When applied in transfer learning mode to endothelial cell classification problems, the algorithm consistently achieves classification accuracies greater than 90% with minimal effort. the The algorithm has been embedded into the open source FARSIGHT toolkit with an intuitive graphical user interface that provides constant feedback about the classification process to the user.Electrical and Computer Engineering, Department o
Immune Profiling and Quantitative Analysis Decipher the Clinical Role of Immune-Checkpoint Expression in the Tumor Immune Microenvironment of DLBCL
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